Application of Modern GIS and Remote Sensing Technology Based on Big Data Analysis in Intelligent Agriculture

Wang, Xiaodi; Yu, Shen; Wen, Zhidan; Zhang, Lijuan; Fang, Chong; Jiang, Lili

doi:10.1007/s12524-022-01512-z

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…Wang et al [71] developed an intelligent agricultural system to process the massive data of agricultural monitoring. The system is divided into three sub-systems: the remote sensing agricultural situation monitoring and information service system, which includes growth monitoring and yield estimation; the remote sensing system for monitoring the resource environment and natural disasters; and the monitoring and supervision system for new rural development and key agricultural construction, which includes new rural construction and low-yield fields.…”

Section: Wang and Mumentioning

confidence: 99%

“…(3) extraction of information from five-year-old data; (4) government climate forecast; and (5) irrigation system. [91] Optical images, SAR images, VV and VH coefficients Spectral images, RGB [92] Satellite images Spectral images, ASCII, HDF, GeoTiff [67] CSV CSV [68] Images, video, audio, telemetry data, user data Documents, RGB image, CSV, videos [69] Images RGB image [71] Images, GPS, documents data RGB image, points, PDF…”

Section: Unstructured Datamentioning

confidence: 99%

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Data Type and Data Sources for Agricultural Big Data and Machine Learning

et al. 2022

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Sustainable agriculture is currently being challenged under climate change scenarios since extreme environmental processes disrupt and diminish global food production. For example, drought-induced increases in plant diseases and rainfall caused a decrease in food production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow analyzing a large amount of data to understand agricultural production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow the processing and analysis of large amounts of heterogeneous data for which intelligent IT and high-resolution remote sensing techniques are required. However, the selection of ML algorithms depends on the types of data to be used. Therefore, agricultural scientists need to understand the data and the sources from which they are derived. These data can be structured, such as temperature and humidity data, which are usually numerical (e.g., float); semi-structured, such as those from spreadsheets and information repositories, since these data types are not previously defined and are stored in No-SQL databases; and unstructured, such as those from files such as PDF, TIFF, and satellite images, since they have not been processed and therefore are not stored in any database but in repositories (e.g., Hadoop). This study provides insight into the data types used in Agricultural Big Data along with their main challenges and trends. It analyzes 43 papers selected through the protocol proposed by Kitchenham and Charters and validated with the PRISMA criteria. It was found that the primary data sources are Databases, Sensors, Cameras, GPS, and Remote Sensing, which capture data stored in Platforms such as Hadoop, Cloud Computing, and Google Earth Engine. In the future, Data Lakes will allow for data integration across different platforms, as they provide representation models of other data types and the relationships between them, improving the quality of the data to be integrated.

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Section: Wang and Mumentioning

confidence: 99%

Section: Unstructured Datamentioning

confidence: 99%

Data Type and Data Sources for Agricultural Big Data and Machine Learning

et al. 2022

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“…Remote sensing technology has played an important role in agricultural production management, mainly for crop distribution and area, growth, yield, disasters, etc. [16][17][18][19][20]. Bolton et al [21] estimated corn and soybean yields in the central United States using the enhanced vegetation index 2 (EVI2) and normalized difference water index (NDWI) that were calculated using the modified resolution imaging spectroradiometer (MODIS) product.…”

Section: Introductionmentioning

confidence: 99%

A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data

Li,

Wang,

Zhang

et al. 2023

Agronomy

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Alfalfa (Medicago sativa L.) is a widely planted perennial legume forage plant with excellent quality and high yield. In production, it is very important to determine alfalfa growth dynamics and forage yield in a timely and accurate manner. This study focused on inverse algorithms for predicting alfalfa forage yield in large-scale alfalfa production. We carried out forage yield and aboveground biomass (AGB) field surveys at different times in 2022. The correlations among the reflectance of different satellite remote sensing bands, vegetation indices, and alfalfa forage yield/AGB were analyzed, additionally the suitable bands and vegetation indices for alfalfa forage yield inversion algorithms were screened, and the performance of the statistical models and machine learning (ML) algorithms for alfalfa forage yield inversion were comparatively analyzed. The results showed that (1) regarding different harvest times, the alfalfa forage yield inversion model for first-harvest alfalfa had relatively large differences in growth, and the simulation accuracy of the alfalfa forage yield inversion model was higher than that for the other harvest times, with the growth of the second- and third-harvest alfalfa being more homogeneous and the simulation accuracy of the forage yield inversion model being relatively low. (2) In the alfalfa forage yield inversion model based on a single parameter, the moisture-related vegetation indices, such as the global vegetation moisture index (GVMI), normalized difference water index (NDWI) and normalized difference infrared index (NDII), had higher coefficients of correlation with alfalfa forage yield/AGB, and the coefficients of correlation R2 values for the first-harvest alfalfa were greater than 0.50, with the NDWI correlation being the best with an R2 value of 0.60. (3) For the alfalfa forage yield inversion model constructed with vegetation indices and band reflectance as multiparameter variables, the random forest (RF) and support vector machine (SVM) simulation accuracy was higher than that of the alfalfa forage yield inversion model based on a single parameter; the first-harvest alfalfa R2 values based on the multiparameter RF and SVM models were both 0.65, the root mean square errors (RMSEs) were 329.74 g/m2 and 332.32 g/m2, and the biases were −0.47 g/m2 and −2.24 g/m2, respectively. The vegetation indices related to plant water content can be considered using a single parameter inversion model for alfalfa forage yield, the vegetation indices and band reflectance can be considered using a multiparameter inversion model for alfalfa forage yield, and ML algorithms are also an optimal choice. The findings in this study can provide technical support for the effective and strategic production management of large-scale alfalfa.

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Application of Modern GIS and Remote Sensing Technology Based on Big Data Analysis in Intelligent Agriculture

Cited by 2 publications

References 18 publications

Data Type and Data Sources for Agricultural Big Data and Machine Learning

Data Type and Data Sources for Agricultural Big Data and Machine Learning

A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data

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